Action recognition in visual environments has advanced greatly thanks to artificial intelligence, but challenges persist when dealing with novel verb-object combinations never seen during training. In this context, compositional zero-shot recognition aims to identify actions such as 'pouring water' or 'cutting bread' even though the model has never observed that exact pair, only the individual elements. However, a subtle trap appears: systems tend to rely on the labeled object class to predict the verb, ignoring the temporal sequence that actually defines the action. This phenomenon, known as the object-based shortcut, limits the generalization ability of models and generates systematic errors in real-world scenarios.
To mitigate these shortcuts, recent research proposes regularization strategies that penalize spurious correlations between verbs and objects, forcing the model to attend to temporal signals. One promising line involves using training pairs with infrequent occurrences as hard negatives, while simultaneously reinforcing sensitivity to the temporal order of frames. In this way, verb representations become more robust and less dependent on the object context. At Q2BSTUDIO, as a software and technology development company, we apply similar principles in our custom software projects, where artificial intelligence is trained with balanced data and strategies that avoid unwanted biases, achieving more reliable systems for production environments.
The practical application of these techniques goes beyond academic research. In the business realm, building models that generalize correctly from few examples is key for computer vision tasks, industrial automation, or video analysis. A model that learns object-based shortcuts could, for example, confuse 'pouring milk' with 'pouring water' simply because the object is a container, when the action is the same. To avoid this, it is necessary to integrate compositional regularization mechanisms into the development pipeline. At Q2BSTUDIO we offer AI for businesses that includes designing robust architectures against data biases, as well as business intelligence services with Power BI to visualize and monitor the behavior of these models.
Furthermore, deploying these systems requires scalable and secure infrastructure. AWS and Azure cloud services allow training complex models with large volumes of data, while cybersecurity ensures that sensitive customer data is protected. At Q2BSTUDIO we develop custom applications that integrate AI agents capable of recognizing actions in real time, and we provide consulting to optimize the performance of these systems in the cloud. The combination of compositional regularization techniques, together with a solid cloud infrastructure, allows companies to obtain computer vision systems that are not only accurate, but also explainable and free of dangerous shortcuts.
